Technology Focus: Bit Technology and Bottomhole Assemblies (December 2011)
Bibliographic record
Abstract
Technology Focus The last decade has seen significant change in many areas of the drilling business, particularly with bits and bottomhole assemblies. Rising drilling costs, more-complex and -demanding drilling environments, and the ever-present stimulus of provider competition are continuing to drive improved understanding and decision making in this area. The days when bits were seen as simple commodities, with their leverage on well time and cost unrecognized, are fading. And this is long overdue. Particularly encouraging is the growing use of field-behavior modeling of the bit and drillstring under realistic conditions, and the development of knowledge-based tool-selection techniques, refined by an intensive study of field data. The migration toward deeper or more-tortuous well designs, often accompanied by simultaneous drilling and hole opening in regions in which vibration effects are prolific and are more punishing, is leading to more understanding and rigor. These are admirable trends that more-traditional operations can and should capitalize on, and sometimes are. My learned colleague Graham Mensa-Wilmot wrote of these trends a year ago in this feature, correctly pointing out to us that “We have the key, let’s open the door.” Perhaps we can claim to have done so with some challenges and in some geographical areas (e.g., vibration diagnosis and mitigation in deepwater Gulf of Mexico operations). However, with other equally important challenges (quantitative optimizing of the rate of penetration comes to mind), fundamental understanding and rigorous methods are not so widespread; we are still operating with “pockets of excellence.” So, it is appropriate to lay another challenge to those managing drilling operations and providing drilling services—if your teams are relying on a fuzzy definition of downhole processes or on trial and error to deliver drilling performance, it is time to modernize—let’s have the current pockets of excellence show the rest of us the way. Bit Technology and Bottomhole Assemblies additional reading available at OnePetro: www.onepetro.org SPE 142431 • “A Practical, Application-Based Guide to Borehole-Enlargement-Tool Selection” by J. McCarthy, SPE, National Oilwell Varco, et al. SPE 146732 • “Geosteering With Sonic in Conventional and Unconventional Reservoirs” by Jason Pitcher, Halliburton, et al. SPE 140069 • “Exploration of the Interaction Between Drill-Bit-Gauge Profile and Point-The-Bit Rotary-Steerable Systems” by L.A. Lines, SPE, Weatherford, et al.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.138 | 0.062 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".